[1]黄钢石,张亚非,陆建江,等.一种受限非负矩阵分解方法[J].东南大学学报(自然科学版),2004,34(2):189-193.[doi:10.3969/j.issn.1001-0505.2004.02.011]
 Huang Gangshi,Zhang Yafei,Lu Jianjiang,et al.Constrained factorization method for non-negative matrix[J].Journal of Southeast University (Natural Science Edition),2004,34(2):189-193.[doi:10.3969/j.issn.1001-0505.2004.02.011]
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一种受限非负矩阵分解方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
34
期数:
2004年第2期
页码:
189-193
栏目:
自动化
出版日期:
2004-03-20

文章信息/Info

Title:
Constrained factorization method for non-negative matrix
作者:
黄钢石1 张亚非1 陆建江123 徐宝文23
1 解放军理工大学通信工程学院, 南京 210007; 2 东南大学计算机科学与工程系, 南京 210096; 3 江苏省软件质量研究所, 南京 210096
Author(s):
Huang Gangshi1 Zhang Yafei1 Lu Jianjiang123 Xu Baowen23
1 Institute of Communication Engineering, PLA University of Science and Technology, Nanjing 210007, China
2 Department of Computer Science and Engineering, Southeast University, Nanjing 210096, China
3 Jiangsu Inst
关键词:
非负矩阵分解 受限非负矩阵分解 潜在语义 信息检索
Keywords:
non-negative matrix factorization constrained non-negative matrix factorization latent semantic relations information retrieval
分类号:
TP18
DOI:
10.3969/j.issn.1001-0505.2004.02.011
摘要:
提出一种获取潜在语义的受限非负矩阵分解方法.通过在非负矩阵分解方法的目标函数上增加3个约束条件来定义受限非负矩阵分解方法的目标函数,给出求解受限非负矩阵分解方法目标函数的迭代规则,并证明迭代规则的收敛性.与非负矩阵分解方法相比,受限非负矩阵分解方法能获取尽可能正交的潜在语义.实验表明,受限非负矩阵分解方法在信息检索上的精度优于非负矩阵分解方法.
Abstract:
A novel method, constrained non-negative matrix factorization, is presented to capture the latent semantic relations. The objective function of constrained non-negative matrix factorization is defined by imposing three additional constraints, in addition to the non-negativity constraint in the standard non-negative matrix factorization. The update rules to solve the objective function with these constraints are presented, and its convergence is proved. In contrast to the standard non-negative matrix factorization, the constrained non-negative matrix factorization can capture the semantic relations as orthogonal as possible. The experiments indicate that the constrained non-negative matrix factorization has better precision than the standard non-negative matrix factorization in information retrieval.

参考文献/References:

[1] Lee D D,Seung H S.Learning the parts of objects by non-negative matrix factorization [J].Nature,1999,401:788-791.
[2] Lee D D,Seung H S.Algorithms for non-negative matrix factorization [J].Advances in Neural Information Processing Systems, 2001,13:556-562.
[3] Tsuge S,Shishibori M,Kuroiwa S.Dimensionality reduction using non-negative matrix factorization for information retrieval [A].In:Proceedings of IEEE International Conference on Systems,Man,and Cybernetice[C].Tucson,USA,2001.960-965.
[4] Li S Z,Hou X W,Zhang H J.Learning spatially localized parts-based representation [A].In:Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition [C].Hawaii,USA,2001.207-212.
[5] Lu Jianjiang,Xu Baowen,Yang Hongji.Mining typical user profiles using non-negative matrix factorizationg [A].In:The Ninth International Conference on Distributed Multimedia Systems [C].Florida,USA,2003.105-109.
[6] Lu Jianjiang,Xu Baowen,Huang Gangshi,et al.Matrix dimensionality reduction for mining typical user profiles [J].Journal of Southeast University(English Edition),2003,19(3):231-235.

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备注/Memo

备注/Memo:
基金项目: 国家自然科学基金青年科学基金资助项目(60303024)、国家973规划资助项目(G1999032701)、国家自然科学基金资助项目(60073012).
作者简介: 黄钢石(1969—),男,博士生,工程师,huang_gangshi@sina.com; 张亚非(联系人),男,博士,教授,博士生导师,yf-zhang888@sina.com.
更新日期/Last Update: 2004-03-20